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Found 24 papers, 8 papers with code
Seamless Underwater Navigation with Limited Doppler Velocity Log Measurements
The results illustrate that our proposed method outperforms the baseline loosely and tightly coupled model-based approaches by an average of 96. 15%.
Multiple and Gyro-Free Inertial Datasets
Despite the growing interest in this field and the availability of INS datasets, no datasets are available for gyro-free INS (GFINS) and multiple inertial measurement unit (MIMU) architectures.
Underwater MEMS Gyrocompassing: A Virtual Testing Ground
In underwater navigation, accurate heading information is crucial for accurately and continuously tracking trajectories, especially during extended missions beneath the waves.
Data-Driven Strategies for Coping with Incomplete DVL Measurements
Autonomous underwater vehicles are specialized platforms engineered for deep underwater operations.
DoorINet: A Deep-Learning Inertial Framework for Door-Mounted IoT Applications
Relying on the gyroscope readings, the accelerometer readings are used to update the attitude angles, and magnetometer measurements are utilized to update the heading angle.
DVL Calibration using Data-driven Methods
Autonomous underwater vehicles (AUVs) are used in a wide range of underwater applications, ranging from seafloor mapping to industrial operations.
A-KIT: Adaptive Kalman-Informed Transformer
In this paper, we derive and introduce A-KIT, an adaptive Kalman-informed transformer to learn the varying process noise covariance online.
Outlier-Insensitive Kalman Filtering: Theory and Applications
State estimation of dynamical systems from noisy observations is a fundamental task in many applications.
INS/DVL Fusion with DVL Based Acceleration Measurements
Autonomous underwater vehicles (AUVs) are increasingly used in many applications such as oceanographic surveys, mapping, and inspection of underwater structures.
Parametric and State Estimation of Stationary MEMS-IMUs: A Tutorial
Inertial navigation systems (INS) are widely used in almost any operational environment, including aviation, marine, and land vehicles.
Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions
The latter is done by learning some of the fusion filter parameters.
Set-Transformer BeamsNet for AUV Velocity Forecasting in Complete DVL Outage Scenarios
Our ST-BeamsNet estimated the AUV velocity vector with an 8. 547% speed error, which is 26% better than the MA approach.
LiBeamsNet: AUV Velocity Vector Estimation in Situations of Limited DVL Beam Measurements
In such conditions, the vehicle's velocity vector could not be estimated leading to a navigation solution drift and in some situations the AUV is required to abort the mission and return to the surface.
Outlier-Insensitive Kalman Filtering Using NUV Priors
The former was previously proposed for the task of smoothing with outliers and was adapted here to filtering, while both EM and AM obtained the same performance and outperformed the other algorithms, the AM approach is less complex and thus requires 40 percentage less run-time.
A Learning-based approach for bias elimination in low cost gyroscopes
Modern sensors play a pivotal role in many operating platforms, as they manage to track the platform dynamics at a relatively low manufacturing costs.
Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation
The purpose of navigation is to determine the position, velocity, and orientation of manned and autonomous platforms, humans, and animals.
Self-Supervised Monocular Depth Underwater
We suggest several additions to the self-supervised framework to cope with the underwater environment and achieve state-of-the-art results on a challenging forward-looking underwater dataset.
A Unified Filter for Fusion of Multiple Inertial Measurement Units
To cope with those shortcomings, we propose and derive a new filter structure for multiple inertial sensors data fusion: the unified extended Kalman filter (UEKF).
BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation
Both simulation and sea experiments were made to validate the proposed learning approach relative to the model-based approach.
Adaptive Attitude Estimation Using a Hybrid Model-Learning Approach
Attitude determination using the smartphone's inertial sensors poses a major challenge due to the sensor low-performance grade and variate nature of the walking pedestrian.
A Hybrid Model and Learning-Based Adaptive Navigation Filter
In most situations, the process noise is covariance assumed to be constant.
Data-Driven Denoising of Stationary Accelerometer Signals
Modern navigation solutions are largely dependent on the performances of the standalone inertial sensors, especially at times when no external sources are available.
Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models
Providing a metric of uncertainty alongside a state estimate is often crucial when tracking a dynamical system.
Deep Learning based Estimation of Weaving Target Maneuvers
The objective of this paper is to examine the potential of using neural networks in target tracking applications.
